List of Accepted Special Sessions
- ESKM-SS1: Data Science in the Humanities and Social Sciences
- ESKM-SS2: ICT Systems to Enhance the Social Good
- ESKM-SS3: LLMs and Creativity Support
- LTLE-SS1: Under-STEM-ed: Discipline-based educational research in STEM teaching and learning perspectives
- LTLE-SS2: AI-Enhanced Foreign Language Education: Innovations in Learning Technology and Pedagogy
- LTLE-SS3: Enhancing Learning through AI: Adaptive, Data-Driven, and Predictive Approaches
- DSIR-SS1: Trends and Issues in Institutional Research and Learning Analytics for Medical Education in Japan Amid the Rise of Generative AI
- DSIR-SS2: Visualization of Learning Outcomes and Data-Driven Quality Assurance in Higher Education
- DSIR-SS3: Research on Student Support at Universities, Including Dropout Prediction and Prevention
- DSIR-SS4: Inclusive Approaches to Data Science Education for All Learners
- DSIR-SS5: Institutional Research in universities to encourage students to qualify for the national examinations
- SCAI-SS1: Advanced Tools and Methods for Cybersecurity and Digital Forensics
- SCAI-SS2: AI and Machine Learning in Engineering for Resilience, Innovation, and Sustainable Futures
- SCAI-SS3: Information Systems and Artificial Intelligence in Society
- BMOT-SS1: Business Management Systems and Strategies for Regional Economic Revitalization
- BMOT-SS2: Application of Informatics Knowledge to Business
- SBIT-SS1: Latest Issues for Business Management
- DSTM-SS1: Industrial Engineering and Operations Research for Energy, Natural Resource and Environmental Managements
- DSTM-SS2: Decision Science on Decision Making Process
- DSTM-SS3: Organisational Behaviour in Management
- CDEF-SS1: Tables in Financial Documents
- CDEF-SS2: Text Analytics in Economics, Finance, and Management
IIAI AAI 2025 Special Sessions
ESKM-SS1: Data Science in the Humanities and Social Sciences
Organizer: Kentaro Haraguchi (Seinan Gakuin University), Daisuke Ikeda (Kyushu University)
Summary: The advancement of informatics has significantly enhanced data collection and analysis techniques, enabling the processing of large-scale datasets and the quantitative analysis of complex knowledge structures that were previously difficult to handle. These technological developments have expanded the scope of research methodologies that can be applied in the humanities and social sciences, allowing for new data-driven approaches.
Despite this progress, the humanities and social sciences have traditionally relied on specialized domain knowledge and unique analytical frameworks, which have often created barriers to interdisciplinary collaboration. As a result, while the application of data science techniques in these fields has been increasing, their integration into mainstream research remains limited. To advance interdisciplinary research, it is essential to first establish a platform for sharing knowledge and insights across these domains.
This special session aims to provide a platform for sharing research that integrates data science techniques with the humanities and social sciences. In addition to studies at advanced stages, we also welcome presentations on early-stage research, research progress reports, and proposals for future studies.
We invite submissions covering a wide range of topics in the humanities and social sciences. Research utilizing techniques such as machine learning, data mining, natural language processing, network analysis, image processing, and multiple regression analysis, among other informatics-related methodologies, is particularly encouraged.
ESKM-SS2: ICT Systems to Enhance the Social Good
Organizer: Kouichi Hirata (Kyushu Institute of Technology), Atsuko Yamaguchi (Tokyo City University)
Summary: We are facing many challenges locally and/or globally, such as erosion in community, aging population, climate change, pandemics, and so on. Information and communication technology (ICT) systems are expected to play many roles in our future society to tackle or solve these issues.
The special session on “ICT Systems to Enhance the Social Good” is a meeting point for researchers, students, and ICT professionals interested in developing such systems to enhance the social good under such circumstances.
The topics of interest in this special session include, but not limited to; ICT systems about pandemics/epidemics, diseases about crops, or zoonotic diseases; ICT systems for cyber security or privacy protection.
ESKM-SS3: LLMs and Creativity Support
Organizer: Hiroaki Furukawa (The University of Kitakyushu)
Summary: This session discusses about Large Language Models (LLMs) and creativity support systems. LLMs continues to evolve rapidly, moreover it can be expected to play a role in supporting humans in a variety of fields. Particularly noteworthy is the trend toward using LLMs in the fields of creativity support.
Previously, AI was considered difficult to demonstrate creativity. However, LLMs represented by ChatGPT, has enabled imitation of creativity by learning from the vast amount of textual data on the Internet. From the above, LLMs shows great potential for supporting creativity. On the other hand, the research fields of creativity support at the dawn of LLMs should be actively discussed in terms of usefulness and safety. In this session, we would like to deepen the discussion from a broad perspective of creativity support, including methodologies, practical examples of using LLMs, and comparisons of creativity between humans and LLMs. Finally, we hope to further developments in the research fields of creativity support and the return of our knowledge with the public.
LTLE-SS1: Under-STEM-ed: Discipline-based educational research in STEM teaching and learning perspectives
Organizer: Emanuela Marchetti (University of Southern Denmark), Andrea Valente (University of Southern Denmark)
Summary: This workshop aims at promoting an exchange on Discipline-Based Educational Research (DBER) and develop evidence-based research to strengthen learning in STEM subjects such as: natural sciences, mathematics, computer sciences and engineering (Henderson et al 2017). The workshop focuses on developing discipline-specific pedagogies and educational engagement in STEM (Bernstein 2000; Shulman 2005), targeting conceptual knowledge and problem-solving skills, also including non-STEM students taking mandatory STEM courses, often resulting in high failure rates and low engagement (Gueudet et al., 2022).
This workshop welcomes contributions exploring new pedagogical and interdisciplinary approaches, conceptual and technological tools to enable secondary and tertiary education students to develop scientific competences relevant to contemporary society (Agustian et al. 2022, Ye et al 2024), overcoming cognitive and epistemological challenges in STEM subjects.
Themes explored in the workshop include:
• STEM/STEAM learning, teaching at secondary and tertiary education, and in the transition, where interdisciplinarity and creativity support understanding and learning,
• Students’ challenges and learning paths in understanding scientific theoretical concepts, developing problem-solving skills as well as a disciplinary identity,
• Teaching challenges and best practices, pedagogical alignment between learning content and assessment, and discipline-related retention pedagogy,
• Technology in STEM/STEAM learning, development and evaluation of no-code programming environments, AI-based tools, digital learning environments from teachers’ and students’ perspective.
The aim of the workshop is to gather researchers across cultures and continents, exchanging empirical evidence, case studies, new conceptual and technological tools, pedagogical practices and frameworks capable to contribute to the global challenge of strengthening scientific education and students’ scientific profile.
LTLE-SS2: AI-Enhanced Foreign Language Education: Innovations in Learning Technology and Pedagogy
Organizer: Michiyo Oda (Reitaku University), Yuichi Ono (University of Tsukuba)
Summary: With the rapid advancement of artificial intelligence (AI), foreign language education is undergoing a transformation. AI-driven tools such as intelligent tutoring systems, adaptive learning platforms, automated assessment tools, and conversational AI are revolutionizing the way languages are taught and learned. These technologies enhance personalized learning experiences, provide real-time feedback, and create immersive learning environments that facilitate engagement and motivation.
This Special Session aims to bring together researchers, educators, and technology developers to explore the integration of AI in foreign language education. We will discuss the latest innovations, challenges, and future directions in AI-assisted learning environments. The session will include presentations on empirical studies, theoretical models, system designs, and case studies highlighting the impact of AI on language learning effectiveness.
We invite submissions addressing, but not limited to, the following research interests:
1. AI-Powered Language Tutoring Systems
2. Personalized and Adaptive Learning in Foreign Language Education
3. AI-Assisted Pronunciation and Speech Recognition Tools
4. Natural Language Processing (NLP) in Language Learning
5. AI for Automatic Assessment and Feedback
6. Gamification and AI-Driven Language Learning Environments
7. Multimodal AI for Language Learning
8. AI in Cross-Cultural Communication and Intercultural Competence Development
9. Ethical Considerations and Data Privacy in AI-Assisted Language Learning
10. Future Directions of AI in Foreign Language Education
LTLE-SS3: Enhancing Learning through AI: Adaptive, Data-Driven, and Predictive Approaches
Organizer: Chengjiu Yin (Kyushu University), Kossuke Mouri (Hiroshima City University), Yuichi Ono (Universaity of Tsukuba)
Summary: Artificial Intelligence (AI) is transforming modern education by enabling adaptive learning, data-driven instructional design, and predictive models for student success. This workshop explores how AI personalizes learning experiences, enhances instructional strategies through data analytics, and supports early intervention using predictive tools. Through hands-on activities, participants will engage with AI-based platforms, gain insights into leveraging learner data, and develop strategies for integrating AI into their teaching and learning environments. Designed for educators, instructional designers, and EdTech professionals, this session will provide a practical roadmap for implementing AI-driven smart learning environments. By the end of the workshop, attendees will have actionable knowledge to create more personalized, efficient, and impactful learning experiences.
DSIR-SS1: Trends and Issues in Institutional Research and Learning Analytics for Medical Education in Japan Amid the Rise of Generative AI
Organizer: Yoshikazu Asada (Jichi Medical University)
Summary: Institutional Research (IR) and Learning Analytics (LA) play an increasingly important role in the evaluation and advancement of medical education in Japan. One key IR topic is the accreditation of medical education, conducted by the Japan Accreditation Council for Medical Education (JACME) based on global standards. With accreditation results valid for up to seven years, some universities are now undergoing their second evaluation cycle. Another critical issue is the revision of the Model Core Curriculum, which provides a framework for all Japanese universities in structuring their healthcare education programs. The Model Core Curriculum was revised in the 2022 academic year, and its implementation is now underway. Understanding how curricula evolve before and after revisions is essential for improving medical education.
In addition, the growing adoption of technology-enhanced education—accelerated by the COVID-19 pandemic—continues to shape both IR and LA. Synchronous and asynchronous learning methods, data-driven curriculum design, and assessment strategies are evolving rapidly. More recently, the emergence of Generative AI has introduced new possibilities and challenges in medical education, further influencing how data is analyzed and applied.
This symposium welcomes a broad range of research and discussions related to IR and LA in medical education, including but not limited to accreditation, curriculum development, learning outcomes assessment, and the impact of new technologies. While the role of Generative AI is an important consideration, we encourage contributions on various aspects of data-driven decision-making and educational evaluation in the field.
DSIR-SS2: Visualization of Learning Outcomes and Data-Driven Quality Assurance in Higher Education
Organizer: Yuji Kobayashi (Kyushu Institute of Technology), Tetsuya Oishi (Kyushu Institute of Technology)
Summary: In today’s society, supporting students in engaging in lifelong learning and contributing to society is a critical challenge for higher education institutions. To address this challenge, it is beneficial to utilize Learning Management Systems (LMS) and e-portfolios for visualizing learning outcomes and to leverage educational data for quality assurance.
The visualization of learning outcomes involves analyzing LMS logs and artifacts to gain deeper insights into students’ learning and growth. By employing learning analytics (LA), educators can refine pedagogical strategies based on data-driven insights, thereby contributing to learner-centered educational improvements.
Moreover, educational data play a crucial role in institutional research (IR) activities, supporting decision-making in educational policy development and enhancing transparency in university governance. Efforts to visualize learning outcomes through educational data are essential for advancing educational practices and strengthening quality assurance measures across institutions.
This session invites case studies and research findings related to the following themes:
• Learning outcomes and their visualization utilizing LMS data and e-portfolios
• Learning analytics and educational practices based on learning behavior and performance data
• Utilization of educational data in IR activities and its application to university governance
• Practical methodologies for quality assurance through the visualization of learning outcomes
• Faculty Development (FD) initiatives to support the analysis and use of learning outcomes data
We invite participants to engage in discussions about how the visualization of learning outcomes contributes to educational improvement and quality assurance, and to foster information sharing and collaborative efforts.
DSIR-SS3: Research on Student Support at Universities, Including Dropout Prediction and Prevention
Organizer: Naruhiko Shiratori (Tokyo City University)
Summary: In higher education, predicting and preventing student dropout is a critical issue, but effective student support strategies go beyond just reducing dropout rates. Providing robust academic support, identifying at-risk students early, and implementing data-informed interventions are vital for enhancing student success and academic outcomes.
This special session invites research that addresses not only dropout prediction and prevention but also broader strategies aimed at fostering student success. Possible topics include using learning analytics to identify at-risk students, designing and evaluating targeted support programs, developing and assessing institutional policies to promote student achievement, and conducting cross-institutional analyses to guide policy-making.
We welcome diverse methodologies and levels of analysis, ranging from micro-level (e.g., learning analytics, early warning systems at the individual student level) to macro-level (e.g., departmental, institutional, or nationwide strategies). Through this session, we hope to advance discussions on data-driven approaches that empower IR professionals and decision-makers, ultimately leading to greater student success and improved retention in higher education.
DSIR-SS4: Inclusive Approaches to Data Science Education for All Learners
Organizer: Eriko Tanaka (Nihon University) and Takaaki Ohkawauchi (Nihon University)
Summary: In recent years, the remarkable advancements in AI and the progress of Big Data technologies that support it have significantly heightened the importance of data science education. The scope of this education has expanded beyond STEM students to include non-STEM students, and there is a growing demand for introducing this education at earlier stages, including elementary schools.
For example, in Japan, the Ministry of Education, Culture, Sports, Science and Technology (MEXT) has introduced the “Approved Program for Mathematics, Data Science, and AI Smart Higher Education, designated by the Government of Japan (MDASH)” certification system, which mandates that universities offer data science education to all enrolled students. As a result, more than half of universities have incorporated data science education, and in form, the flow of data science education is gradually taking shape. However, several challenges remain, as outlined below:
・Barriers to learning due to the relevance to students’ major fields of study
・A shortage of qualified instructors capable of delivering appropriate courses
・Whether to incorporate ICT tool utilization skills in the curriculum
・Differentiation of curricula at various educational stages, such as elementary, middle, and high schools, and universities
・The evaluation of the effectiveness of data science education
This session aims to broadly gather practices from educational institutions that have already begun data science education, raise pertinent challenges, and present data analysis examples. Through subsequent discussions, we aim to explore data science education for all individuals.
DSIR-SS5: Institutional Research in universities to encourage students to qualify for the national examinations
Organizer: Kenjiro Sakaki (Tenshi College), Kunihiko Takamatsu (Institute of Science Tokyo)
Summary: In 2012, the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) proposed the establishment of Institutional Research (IR) in Japanese universities. Most universities have since launched this department. One of the objectives of the IR department is to reform the education system in order to advance higher education. Universities can be classified into two categories. The first type is designed to enable students to qualify for the national examination. The second type includes the universities excluding those under the first type. We can refer to the former as a national license-type university. MEXT promotes higher education reforms in universities. However, the curriculum of the national license-type university has to conform with the rules in order to obtain a qualification for the national examination from the corresponding ministry. Hence, there should be a difference between the IR in a license-type university and a non-license-type university. We will discuss the differences and similarities between the IR in license and non-license-type universities.
SCAI-SS1: Advanced Tools and Methods for Cybersecurity and Digital Forensics
Organizer: Jaouhar Fattahi (Laval University) and Mohamed Mejri (Laval University) and Mariam Wajdi Ibrahim (German Jordanian University)
Summary: As communication networks become increasingly complex, ensuring robust security—particularly in forensic investigations and AI-driven cybersecurity—is both essential and challenging. Traditional security solutions often fail to meet the demands of dynamic, distributed, and heterogeneous environments, highlighting the need for advanced frameworks that enhance security while fostering collaboration across diverse applications.This session brings together researchers and practitioners in cybersecurity, digital forensics, and cybercrime prevention to explore innovative methods for secure system design, forensic analysis, and AI-based threat detection. Contributions integrating formal methods, AI, ML, DL, XAI, and NLP to advance cybersecurity solutions are encouraged.
Topics of Interest Include:
-Cryptographic protocols and quantum cryptography
-AI applications in cybersecurity and digital forensics
-Machine learning for threat detection and forensic insights
-Security in networks, hardware, and software
-Biometric authentication and identity verification
-Intrusion detection and anomaly detection systems
-Security in web applications and emerging technologies
-Privacy, trust, anonymity, and access control frameworks
-5G/6G security, LiFi, and autonomous system protection
-Deep learning and XAI for cybersecurity and forensic analysis
This special session serves as a collaborative platform to address evolving challenges in cybersecurity, forensic investigations, and AI-driven cyber defense, fostering the development of secure and resilient digital ecosystems.
SCAI-SS2: AI and Machine Learning in Engineering for Resilience, Innovation, and Sustainable Futures
Organizer: Cheng Chin (Newcastle University)
Summary: The integration of artificial intelligence (AI) and machine learning (ML) into engineering disciplines has revolutionized design, optimization, predictive maintenance, and decision-making processes across industries. This special session aims to explore cutting-edge advancements, practical applications, and interdisciplinary synergies at the intersection of AI/ML and engineering. Topics of interest include (but are not limited to) AI-driven predictive modeling, autonomous systems, intelligent automation, digital twins, reinforcement learning for dynamic systems, and ethical considerations in AI-augmented engineering. The session will also address challenges such as data scarcity, interpretability, scalability, and real-world deployment. By fostering dialogue among researchers, practitioners, and industry leaders, this session seeks to highlight transformative case studies, emerging methodologies, and collaborative pathways to address global engineering challenges. Contributions are encouraged to emphasize both theoretical rigor and practical relevance, with a focus on sustainable, scalable, and human-centric solutions. This forum will serve as a catalyst for bridging gaps between academia and industry while inspiring innovations that redefine the future of engineering.
SCAI-SS3: Information Systems and Artificial Intelligence in Society
Organizer: Kazunori Iwata (Aichi University), Nobuhiro Ito (Aichi Institute of Technology), Takeshi Uchitane (Aichi Institute of Technology)
Summary: This special session covers research on artificial intelligence and applied information systems in society. Such research is also expected to include evaluation results when used in actual social or simulation fields. Numerous information systems and artificial intelligence technologies have been developed in recent years for real-world society. These technologies have been profoundly and widely adopted in infrastructure and social life, seamlessly integrating into everyday human activities. In the background of their developments, revolutionary and intelligent technologies are still being developed to make excellent software systems. Hence, this session welcomes the research work in the following topics, but not limited to: (1) Intelligent applications and analysis in society; (2) Intelligent educational systems; (3) Hybrid sensor systems using artificial intelligence; (4) Multi-agent systems; (5) Multi-agent simulation technologies and coordination mechanisms.
BMOT-SS1: Business Management Systems and Strategies for Regional Economic Revitalization
Organizer: Hidekazu Iwamoto (Josai International University)
Summary: This special session offers business management systems and strategies for the revitalization of regional economies. Based on all aspects (theories, applications, and tools) of business management systems and strategies, the special session will discuss the practical challenges associated with this topic.
BMOT-SS2: Application of Informatics Knowledge to Business
Organizer: Shimpei Matsumoto (Hiroshima Inst. of Tech.)
Summary: In recent years, the application of informatics knowledge to business has gained significant attention, transforming industries through advanced computational techniques. This special session aims to explore how various informatics-based methodologies, including cognitive science, machine learning, and other computational approaches, are being utilized to enhance business operations, decision-making, and innovation. Speakers will present case studies and research findings that highlight the successful integration of informatics techniques in real-world business scenarios. Topics may include the use of cognitive computing for customer behavior analysis, the application of machine learning in predictive analytics, and the optimization of business processes through data-driven strategies. By bridging the gap between cutting-edge informatics research and practical business applications, this session seeks to provide insights into emerging trends and foster discussions on how businesses can leverage these technologies for competitive advantage. We invite researchers, industry professionals, and practitioners to join us in this engaging exchange of ideas and innovations.
SBIT-SS1: Latest Issues for Business Management
Organizer: Hiroyuki Ono (Chiba Institute of Technology)
Abstract:In recent years, with the development of ICT, companies are required to carry out activities in all aspects such as improving business efficiency and collaborating with other companies and so on. Furthermore, it is necessary to create new services for the future based on various information not only from companies but also from outside. In addition, as we enter the corona era and our lifestyle is changing rapidly, we need to build an enterprise structure that quickly responds to the external environment. The purpose of this session is to share information on solutions and evaluation methods by taking up various problems in management work that are necessary in various aspects of business management. Moreover, we expect to accelerate the studies and to create a new research field through the introduction of best practice, failure case, and work-in-progress issues by contributed papers.
DSTM-SS1: Industrial Engineering and Operations Research for Energy, Natural Resource and Environmental Managements
Organizer: Ryuta Takashima (Tokyo University of Science)
Summary: Firms in the energy and natural resource sectors are subject to various risks and uncertainties, including cost fluctuations, market dynamics, supply chain disruptions, and regulatory changes. In particular, the increasing concerns over climate change have led to the implementation of various policies aimed at reducing greenhouse gas emissions. Consequently, firms must integrate environmental policies and regulations into their project operations. To address these decision-making challenges, firms and policymakers can leverage theories and methodologies from industrial engineering and operations research. This session covers the application of such methodologies to energy, natural resource, and environmental management. Specifically, we will include the following research topics:
1. Investment in power generation and transmission under uncertainty
2. Risk management in power purchase agreements
3. Decision-making in sustainable forest management
4. Optimization models for hydrogen supply networks and production technology deployment
5. Energy production planning models integrating resource supply-demand network optimization and decarbonization technology selection
Through an analysis of these topics, we discuss the applicability of industrial engineering and operations research methods to sustainable and resilient management practices in the energy, natural resource, and environmental sectors.
DSTM-SS2: Decision Science on Decision Making Process
Organizer: Takaaki Hosoda (Advanced Institute of Industrial Technology)
Summary: Research on the decision-making process explores how individuals or groups make choices. It examines factors influencing decisions, such as cognitive biases, emotions, and social influences. Studies often analyze decision-making models, strategies, and their outcomes. Understanding this process can lead to insights into improving decision-making effectiveness in various contexts, including business, psychology, and public policy. This special session focuses on the decision-making process for human activity. Based on several theories, this session aims to discuss various topics about decision making.
DSTM-SS3: Organisational Behaviour in Management
Organizer: Morihiko Ikemizu (Advanced Institute of Industrial Technology)
Summary: Organizational behavior theory is an academic field that studies the behavior and interactions of individuals and groups within organizations, and the factors that influence them. This field seeks to better understand how people behave in organizations, why they behave in certain ways, and the impact this has on the organization as a whole. This session will specifically address effective leadership, teamwork, and motivation within organizations.
CDEF-SS1: Tables in Financial Documents
Organizer: Yasutomo Kimura (Otaru University of Commerce), Kazuma Kadowaki (The Japan Research Institute, Limited), Hokuto Ototake (Fukuoka University)
Summary: This special session is organized as a forum to discuss cutting-edge research and technological innovations in tasks related to financial documents. Financial documents are characterized by their vast and diverse data, primarily written in natural language, and often containing tabular information. However, traditional natural language processing (NLP)-based extraction and analysis methods often struggle to handle tabular data effectively. In this session, we invite research presentations that focus on various tasks from a technical perspective. Topics of interest include extracting tables from annual securities reports, question-answering and summarization based on financial tables, machine reading of financial documents, and creating datasets for inter-company comparisons. We welcome contributions from a wide range of research areas, including evaluation experiments using real-world data, case studies applying new machine learning algorithms, and practical solutions addressing technical challenges with real-world applications. Through exchanging ideas among participants, we aim to contribute to advancing financial information analysis and strengthen collaboration with industry.
CDEF-SS2: Text Analytics in Economics, Finance, and Management
Organizer: Atsushi Keyaki (Hitotsubashi University), Toshiaki Watanabe (Hitotsubashi University), Chung-Chi Chen (National Institute of Advanced Industrial Science and Technology), Susumu Nagayama (Hitotsubashi University)
Summary: In this session, we invite research contributions that apply a diverse array of text analysis techniques to topics in the fields of Economics, Finance, and Management. Submissions may employ methods such as lexicon and dictionary-based approaches, rule-based methods, machine learning models, deep learning architectures, and large language models (LLMs) to extract meaningful insights from textual data. We encourage studies that both advance theoretical understanding and demonstrate practical applications in real-world contexts, showcasing the transformative potential of these techniques.
Our call welcomes work from multiple disciplines, including natural language processing and text mining research from computer science, data science methodologies from statistics, and innovative applications from the fields of economics, finance, and management. However, the scope is not limited to these areas; we are interested in any research that leverages text analysis to address pressing challenges or to uncover new perspectives within these domains.
This session aims to bridge the gap between advanced computational techniques and their applications in economic and financial analysis and managerial decision-making. By fostering interdisciplinary dialogue, we hope to stimulate collaboration among researchers and practitioners. Participants are invited to share novel methodologies, comprehensive case studies, and theoretical advancements that collectively contribute to a deeper understanding of textual data and its significant impact on modern economic, financial, and managerial practices.